Using transfer entropy to measure the patterns of information flow though cortex : application to MEG recordings from a visual Simon task

Poster presentation: Functional connectivity of the brain describes the network of correlated activities of different brain areas. However, correlation does not imply causality and most synchronization measures do not di
Poster presentation: Functional connectivity of the brain describes the network of correlated activities of different brain areas. However, correlation does not imply causality and most synchronization measures do not distinguish causal and non-causal interactions among remote brain areas, i.e. determine the effective connectivity [1]. Identification of causal interactions in brain networks is fundamental to understanding the processing of information. Attempts at unveiling signs of functional or effective connectivity from non-invasive Magneto-/Electroencephalographic (M/EEG) recordings at the sensor level are hampered by volume conduction leading to correlated sensor signals without the presence of effective connectivity. Here, we make use of the transfer entropy (TE) concept to establish effective connectivity. The formalism of TE has been proposed as a rigorous quantification of the information flow among systems in interaction and is a natural generalization of mutual information [2]. In contrast to Granger causality, TE is a non-linear measure and not influenced by volume conduction. ...
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Metadaten
Author:Michael Wibral, Raul Vicente, Jochen Triesch, Gordon Pipa
URN:urn:nbn:de:hebis:30-70795
Document Type:Article
Language:English
Date of Publication (online):2009/09/20
Year of first Publication:2009
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2009/09/20
Note:
© 2009 Wibral et al; licensee BioMed Central Ltd.
Source:BMC Neuroscience 2009, 10(Suppl 1):P232 ; doi:10.1186/1471-2202-10-S1-P232 ; from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009
HeBIS PPN:21898491X
Institutes:Medizin
Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Sondersammelgebiets-Volltexte
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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